Tractor powertrain joint optimization method and system based on economic indicators

Abstract

The present disclosure provides a combined optimization method and system for a tractor powertrain based on economic indicators, relating to the field of tractor control technology, including: based on the structural information and operating conditions information of a target tractor, building a tractor powertrain model by forward simulation; using the tractor powertrain model to perform feedback control on the actual vehicle speed of the target tractor following the desired vehicle speed; during the control process, taking the actual fuel consumption rate per unit cultivated land area as the vehicle fuel economy indicator, performing economic combined optimization on the powertrain control parameters to obtain the optimal powertrain control parameters of the target tractor under the optimal economic indicator; through the tractor powertrain model, taking the vehicle fuel economy indicator as the evaluation criterion, comprehensively optimizing the engine fuel injection parameters and the displacement ratio of the hydro-mechanical continuously variable transmission, obtaining the optimal solution of the tractor's comprehensive energy consumption, and effectively improving the vehicle fuel economy performance.

Description

Technical Field

[0001] The present disclosure relates to the technical field of tractor control, and particularly to a combined optimization method and system for a tractor powertrain based on economic indicators. Background Art

[0002] With the continuous advancement of the urban modernization process, the contradiction between the reduction of agricultural labor and the increasing demand for crops has become increasingly prominent. Among them, large-horsepower, high-efficiency intelligent tractors play an important role in improving agricultural work efficiency and quality. The accelerating improvement of tractor technology has also brought about a large number of problems such as fuel consumption and environmental pollution, making the requirement for green large-horsepower, high-efficiency intelligent tractors with low pollutants more urgent. Among them, the powertrain system composed of an engine and a transmission, as an important unit of tractor power transmission, its power generation and transmission performance will directly affect the power performance, economy, green and low-carbon performance, etc. of agricultural machinery during operation.

[0003] The engine industry takes the application of low-carbon fuels as the main development direction in the aspect of green and low-carbon development. The dual-fuel mode of diesel + alternative fuel is one of the important ways for traditional diesel engines to reduce the dependence on fossil fuels while meeting traditional emission regulations; among them, dual-fuel engines are further divided into in-cylinder high-pressure direct injection (HPDI) and in-cylinder low-pressure direct injection according to the position of the piston when fuel is injected. Compared with each other, low-pressure direct injection has the advantage of lower NOx and soot emissions, but the problems of low-load knock limiting the engine power performance and hydrocarbon emissions need to be further solved. Due to the higher injection pressure, the HPDI engine improves the charging efficiency, and the diffusion-based combustion mode can effectively overcome the knock problem of the premixed natural gas engine under high load. In the agricultural and industrial fields, research institutions have compared the fuel consumption rates and emission characteristics of diesel engines and diesel / natural gas dual-fuel engines, and the results show that diesel / natural gas dual-fuel engines have better fuel economy and significantly reduced nitrogen oxide emissions.

[0004] As an important part of tractor power transmission, the output torque and speed of the transmission need to be continuously adjusted to adapt to the changes in external loads. Against this background, the application of continuously variable transmissions (CVTs) has become a trend. The most common types of CVTs are hydrostatic transmissions (HSTs) and hydro-mechanical continuously variable transmissions (HMCVTs). Among them, the transmission efficiency of HSTs is much lower than that of gear transmissions, resulting in it being rarely used in large-horsepower tractors. To overcome the defects of HSTs, HSTs are connected in parallel with mechanical components to form a hydro-mechanical continuously variable transmission (HMCVT), where the hydraulic part only transmits part of the power, and the remaining power is transmitted through mechanical components. This can improve the low transmission efficiency while having better driving comfort. It can achieve stepless speed change while transmitting high power, and has broad development prospects in high-power agricultural machinery equipment.

[0005] Currently, for one - dimensional or zero - dimensional engine modeling, the internal characteristics of the engine are usually analyzed and studied. The average parameter model and the model based on experimental data are usually coupled with the transmission for the research of the powertrain control strategy. At present, there is still a lack of relevant control research on the coupling of one - dimensional or zero - dimensional alternative fuel engine models with the powertrain, and there is also a lack of relevant research on the control strategy of the coupling of key internal engine parameters with the powertrain. Many researchers use the experimental Map diagram to equivalently establish the engine model and couple it with the transmission model to design and optimize the energy management strategy of the tractor powertrain. This method can find the optimal economic torque speed of the engine and the transmission ratio at each vehicle speed point, but it has a strong dependence on experimental data. At the same time, the engine Map diagram modeling does not consider the influence of changes in key internal engine parameters on the fuel consumption rate and output torque, thus losing the possibility of considering the influence of key internal engine parameters on the powertrain energy management strategy.

[0006] The economy of the tractor is jointly affected by the engine and the transmission. The existing technology lacks research on the dynamic and economic characteristics under the coupling effect of key internal engine parameters and the power split of the hydro - mechanical continuously variable transmission (HMCVT), resulting in poor optimization effects of the powertrain including the green fuel engine and the hydro - mechanical continuously variable transmission (HMCVT). Summary of the Invention

[0007] In order to solve the above problems, the present disclosure proposes a method and system for jointly optimizing the tractor powertrain based on economic indicators. By using the tractor powertrain model and taking the vehicle - level fuel economy indicator as the evaluation criterion, the fuel injection parameters of the engine and the displacement ratio of the hydro - mechanical continuously variable transmission are comprehensively optimized to obtain the optimal solution for the comprehensive energy consumption of the tractor, effectively improving the vehicle - level fuel economy performance.

[0008] According to some embodiments, the present disclosure adopts the following technical solutions:

[0009] A method for jointly optimizing the tractor powertrain based on economic indicators, comprising:

[0010] Based on the structural information and operating conditions information of the target tractor, a tractor powertrain model is built by forward simulation;

[0011] Using the tractor powertrain model, following the desired vehicle speed, feedback control is performed on the actual vehicle speed of the target tractor;

[0012] Among them, during the control process, the actual fuel consumption rate per unit cultivated land area is used as the vehicle fuel economy index, and the control parameters of the powertrain are jointly optimized for economy to obtain the best powertrain control parameters of the target tractor under the optimal economy index. The powertrain control parameters include the fuel injection parameters of the engine and the displacement ratio of the hydro-mechanical continuously variable transmission.

[0013] According to some embodiments, the present disclosure adopts the following technical solutions:

[0014] A tractor powertrain joint optimization system based on economy index, comprising:

[0015] A model construction module, configured to: based on the structural information and working condition information of the target tractor, build a tractor powertrain model by forward simulation;

[0016] A vehicle speed control module, configured to: utilize the tractor powertrain model to perform feedback control on the actual vehicle speed of the target tractor following the desired vehicle speed;

[0017] Among them, during the control process, the actual fuel consumption rate per unit cultivated land area is used as the vehicle fuel economy index, and the control parameters of the powertrain are jointly optimized for economy to obtain the best powertrain control parameters of the target tractor under the optimal economy index. The powertrain control parameters include the fuel injection parameters of the engine and the displacement ratio of the hydro-mechanical continuously variable transmission.

[0018] According to some embodiments, the present disclosure adopts the following technical solutions:

[0019] A computer program product, comprising a computer program, where the computer program realizes the tractor powertrain joint optimization method based on economy index when executed by a processor.

[0020] According to some embodiments, the present disclosure adopts the following technical solutions:

[0021] A non-transitory computer-readable storage medium, where the non-transitory computer-readable storage medium is used to store computer instructions, and when the computer instructions are executed by a processor, the tractor powertrain joint optimization method based on economy index is realized.

[0022] According to some embodiments, the present disclosure adopts the following technical solutions:

[0023] An electronic device, comprising: a processor, a memory, and a computer program; wherein, the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device runs, the processor executes the computer program stored in the memory so that the electronic device executes and realizes the tractor powertrain joint optimization method based on economy index.

[0024] Compared with the prior art, the beneficial effects of the present disclosure are as follows:

[0025] The present invention incorporates actual working condition factors such as cultivated land area into the economic index, thereby constructing a vehicle fuel economy index, which can more accurately evaluate the fuel consumption and overall economic changes of tractors under different working conditions, and thus provide more targeted data support for optimized design and improved agricultural production efficiency.

[0026] The present invention establishes a tractor powertrain model including a plowing working condition model, an HMCVT controller, a driver model, a powertrain model, and a vehicle dynamics model of the tractor. With the goal of minimizing the vehicle fuel economy index, comprehensive optimization is carried out on the key fuel injection parameters inside the engine, as well as the displacement ratio and speed ratio of the HMCVT. Through joint optimization, the vehicle fuel economy index is significantly improved, the overall combined fuel consumption decreases, the actual specific fuel consumption is reduced by up to 18.06%, and the instantaneous specific fuel consumption per unit area is reduced by up to 12.26%. By adopting the joint optimization algorithm of the engine and HMCVT, the power cycle problem that cannot be avoided when optimizing the tractor HMCVT alone is successfully solved, and the vehicle fuel economy performance is effectively improved from the perspective of powertrain joint optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] The accompanying drawings forming a part of this disclosure are used to provide a further understanding of the disclosure. The illustrative embodiments and descriptions thereof of the disclosure are used to explain the disclosure and do not constitute an improper limitation of the disclosure.

[0028] Figure 1 It is the plowing route map of Embodiment 1.

[0029] Figure 2 It is the plowing speed curve graph of Embodiment 1.

[0030] Figure 3 It is the verification graph of fuel injection quantity update of Embodiment 1.

[0031] Figure 4 It is the verification graph of air injection quantity update of Embodiment 1.

[0032] Figure 5 It is the verification graph of torque update of Embodiment 1.

[0033] Figure 6 It is the verification graph of rotational speed update of Embodiment 1.

[0034] Figure 7 It is the structural diagram of the tractor powertrain model of Embodiment 1.

[0035] Figure 8 It is the overall vehicle control strategy flowchart of the tractor powertrain model of Embodiment 1.

[0036] Figure 9 It is a comparison chart of two economic indicators for Example 1.

[0037] Figure 10 It is a schematic diagram showing the influence of fuel injection parameters in Example 1 on the engine output torque.

[0038] Figure 11 It is a schematic diagram of the vehicle fuel economy at different injection times of the engine in Example 1.

[0039] Figure 12 It is a diagram of the economic joint optimization algorithm for Example 1.

[0040] Figure 13 It is the diesel injection quantity in one working cycle of the engine corresponding to different vehicle speeds and HMCVT speed ratios in Example 1.

[0041] Figure 14 It is the natural gas injection quantity in one working cycle corresponding to different vehicle speeds and HMCVT speed ratios in Example 1.

[0042] Figure 15 It is the diesel injection time corresponding to different vehicle speeds and HMCVT speed ratios in Example 1.

[0043] Figure 16 It is the natural gas injection time corresponding to different vehicle speeds and HMCVT speed ratios in Example 1.

[0044] Figure 17 It is the comparison of diesel / natural gas injection times corresponding to different vehicle speeds and HMCVT speed ratios in Example 1.

[0045] Figure 18 It is the interval of diesel / natural gas injection times corresponding to different vehicle speeds and HMCVT speed ratios in Example 1.

[0046] Figure 19 It is the comparison of the vehicle economy before and after optimization with the traditional specific fuel consumption as the observable quantity in Example 1.

[0047] Figure 20 It is the comparison of the vehicle economy before and after optimization with the economic evaluation index as the observable quantity in Example 1. Detailed implementation manners

[0048] The present disclosure will be further described below in conjunction with the accompanying drawings and embodiments.

[0049] It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the present disclosure. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present disclosure belongs.

[0050] Embodiment 1

[0051] In an embodiment of the present disclosure, a combined optimization method for a tractor powertrain based on economic indicators is provided, including:

[0052] Based on the structural information and working conditions information of the target tractor, a tractor powertrain model is built by forward simulation;

[0053] Using the tractor powertrain model, following the desired vehicle speed, feedback control is performed on the actual vehicle speed of the target tractor;

[0054] Among them, during the control process, the actual fuel consumption rate per unit cultivated area is used as the vehicle fuel economy indicator, and the powertrain control parameters are jointly optimized economically to obtain the best powertrain control parameters of the target tractor under the optimal economic indicator. The powertrain control parameters include the fuel injection parameters of the engine and the displacement ratio of the hydro-mechanical continuously variable transmission.

[0055] As an embodiment, for the combined optimization method of the tractor powertrain based on economic indicators of the present disclosure, the implementation process will be described in detail below from the tractor powertrain model, the vehicle fuel economy indicator, and the powertrain economic joint optimization algorithm.

[0056] I. Tractor Powertrain Model

[0057] The plowing operation condition, as the most common working condition of large-horsepower tractors, is representative. Therefore, this condition is taken as an example for analysis; the plowing operation of the tractor is the most typical working condition of the tractor and also the working condition with the largest energy demand. When performing the plowing operation, a normal cycle includes two stages: plowing and parking for turning around. The tractor's plowing route in the field is as Figure 1 shown. When the tractor is operating in the field, its driving speed is affected by many factors such as the working conditions, load status, and soil conditions. Table 1 shows the driving speed range of the tractor in different situations:

[0058] Table 1 Tractor driving speed under different working conditions

[0059]

[0060] The speed of the tractor's plowing operation is generally 5 - 10 km / h. In this embodiment, according to the actual tillage situation of the tractor, it is assumed that the plowing speed is 7 km / h. Since the plowing speed has certain fluctuations during the plowing process, a random fluctuation is added on the basis of the constant plowing vehicle speed as the plowing test condition, and the cycle time under a single condition is 200 s. Figure 2It is the superposition of two plowing cycle conditions. The average speed of a single cycle condition is 5.6 km / h. Among them, the period from 0 to 167 s is the plowing stage, and the average vehicle speed in this stage is 6.5 km / h. The period from 168 to 200 s is the tractor's parking and turning stage, and the average speed in this stage is 1.47 km / h. During the parking and turning stage, the plowing resistance suffered by the tractor is zero, and only the rolling resistance is present.

[0061] Based on the above condition analysis, to more detailedly illustrate the effect of the coupling of the engine and the hydro-mechanical continuously variable transmission (HMCVT) on the power cycle optimization of the HMCVT, in this embodiment, a dual-fuel engine is used as the object for relevant description, and the fuel injection quantity and the gas injection quantity are adjusted accordingly according to the size relationship between the two. Therefore, the fuel injection quantity and the gas injection quantity are used as the input values of the engine model. Figures 3 - 6 They are respectively the fuel injection quantity update verification diagram, the gas injection quantity update verification diagram, the torque update verification diagram, and the rotational speed update verification diagram. As Figure 3 、 Figure 4 shown, they should remain unchanged within each working cycle.

[0062] The engine output torque is obtained based on the effective work done in one engine working cycle. Therefore, the output torque transmitted from the alternative fuel engine model to the HMCVT model is calculated from the effective work obtained in the previous engine working cycle. As Figure 5 shown, it remains unchanged within the current working cycle of the engine. Similarly, as Figure 6 shown, it can be seen that the engine output rotational speed and the output torque maintain the same update frequency. According to the energy signal and the power transmission direction of the large-horsepower tractor, the simulation model can be divided into two types: forward and backward simulation models:

[0063] In the forward simulation model, the energy, signal, and power transmission direction of the tractor is consistent with the actual situation, and a driver model is added to follow the speed of the tractor, which has a high accuracy.

[0064] In the backward simulation model, the energy, signal, and power transmission direction of the tractor is opposite to the actual situation, and it has the characteristic of simple structure. There is no driver model in the backward simulation model. The output torque of the gearbox is calculated through the dynamic model based on the given desired vehicle speed and then transmitted. The backward simulation is an open-loop system, and the simulation accuracy is not accurate enough. Therefore, in this embodiment, forward simulation is adopted to build a power-train model of the large-horsepower tractor. As Figure 7 shown, it includes a plowing condition model, an HMCVT controller, a driver model, a power system model, and a vehicle dynamics model of the large-horsepower tractor. The follow-up of the desired vehicle speed under the plowing condition is realized through the HMCVT controller. The driver model is a mathematical model that imitates the driver's operation. By controlling the engine throttle opening, the follow-up of the engine output torque to the HMCVT required torque is realized. Specifically:

[0065] Step S1.1 The plowing condition model provides an input for the target speed of the plowing operation.

[0066] Step S1.2 The HMCVT controller controls the displacement ratio and the transmission gear according to the difference between the desired vehicle speed and the actual vehicle speed in the plowing condition.

[0067] The driver model is actually an engine throttle opening controller, which calculates and outputs the opening of the throttle pedal according to the difference between the desired vehicle speed and the actual vehicle speed and the difference between the HMCVT required torque and the actual engine output torque.

[0068] In this embodiment, a PID controller is used to implement driver control. The input signal of the PID controller is the gap T(t) between the engine output torque that the engine can provide to the transmission and the torque required by the transmission. The output signal of the PID controller is the throttle opening signal u(t). The specific formula is as follows:

[0069] (1)

[0070] In the formula, T(t) is the gap between the engine output torque that the engine can provide to the transmission and the torque required by the transmission, T HMCVT_req (t) is the required torque of the transmission of the tractor at time t, with the unit of km / h; T eng_out (t) is the output torque of the engine of the tractor at time t, with the unit of km / h.

[0071] (2)

[0072] In the formula, u(t) is the throttle opening signal, where a positive value is defined as an increase in the throttle opening and a negative value is defined as a decrease in the throttle opening; T(t) is the gap between the engine output torque that the engine can provide to the transmission and the torque required by the transmission; k p is the proportionality coefficient; k i is the integral coefficient; k d is the differential coefficient.

[0073] Step S1.4 The power system model includes an engine model, an HMCVT model, a central reducer, and a wheel side reducer, and controls the engine output torque and the HMCVT output speed according to the control signals output by the HMCVT controller and the driver model. The torque of the HMCVT is input to the vehicle dynamics model of the tractor through the transmission system.

[0074] Step S1.5 The vehicle dynamics model calculates the speed at each moment to obtain the actual speed of the tractor.

[0075] The output speed of the HMCVT is converted into the wheel speed through the central reducer and the wheel side reducer. The true speed of the tractor can be calculated by the wheel speed and the wheel radius.

[0076] The vehicle control strategy process of the tractor powertrain model built in this embodiment is as Figure 8 shown, specifically:

[0077] At the beginning, the tractor is in the initial state, the input parameters of the engine and the transmission ratio of the HMCVT are the initial values. Since the HMCVT is in the initial stage of the first gear at this time, the output speed is zero and the vehicle speed is zero.

[0078] When the vehicle speed road spectrum starts to change, the expected vehicle speed v target of the tractor is different from the actual vehicle speed v tractor . First of all, the HMCVT controller controls the output true vehicle speed of the tractor by adjusting the displacement ratio and gear of the hydraulic pump - fixed displacement motor; secondly, according to the dynamic model and the adjusted transmission efficiency of the HMCVT, the required torque T hmcvt_in of the HMCVT is calculated. According to the output torque T eng_out of the engine and the required torque T hmcvt_in of the HMCVT, the throttle opening of the engine is adjusted to adapt to the size of the required torque T hmcvt_in of the HMCVT; finally, the true vehicle speed v tractor is compared with the expected vehicle speed v target . If the two are not equal, the throttle opening of the engine and the displacement ratio of the HMCVT are adjusted in a loop.

[0079] II. Vehicle fuel economy index

[0080] From the perspective of the economic variable speed control strategy, the amount of fuel consumption reflects the economic advantages and disadvantages. If only the best fuel consumption of the engine is used as the economic target to formulate the transmission control strategy, the continuously variable transmission performance of the whole machine cannot be fully reflected. Therefore, in this embodiment, actual working condition factors such as cultivated land area are incorporated into the economic index to construct a vehicle fuel economy index. The specific construction method is as follows:

[0081] According to the characteristics of the tractor towing agricultural implements for operation, the tractor mainly performs field plowing, sowing, rotary tillage and other operations. Its operation mode determines its power output mode. Generally, the fuel consumption during tractor steering is not considered. Therefore, the specific fuel consumption is usually used as the vehicle fuel economy index of the tractor. The actual fuel consumption rate of the tractor under any traction condition, that is, the specific fuel consumption, is expressed as:

[0082] (3)

[0083] In the formula, g eis the specific fuel consumption rate of the engine, η T is the traction efficiency of the tractor.

[0084] It can be analyzed from the above formula that for a tractor performing traction operations, it is necessary to ensure that the specific fuel consumption rate g of the engine e is minimized, and the traction efficiency η T is maximized, so that the fuel economy of the whole vehicle can reach the optimal level.

[0085] Generally speaking, the tractor transmission system mainly includes four parts: HMCVT, central transmission system, wheels, and load system. Power is output from the engine, then passes through the tractor's transmission system, and finally performs traction operations by pulling the load or overcoming ground resistance. Since power losses will occur in each part of the transmission system during the power transmission process, thus, according to the power transmission route, the traction efficiency of the tractor can be obtained:

[0086] (4)

[0087] In the formula, η b is the transmission efficiency of the gearbox; η z is the transmission efficiency of the central reducer; η l is the transmission efficiency of the wheel side reducer.

[0088] The tillage efficiency of the tractor is related to the field size, the working area of the implement, and the tractor performance. Therefore, the instantaneous tillage area of the tractor is introduced into the actual specific fuel consumption rate to obtain a new calculation formula for the specific fuel consumption rate:

[0089] (5)

[0090] Among them, g com_enegy_consum is the specific fuel consumption per unit area, that is, the actual specific fuel consumption per unit tillage area, with the unit of g / (kW·h·km²), g e is the specific fuel consumption rate of the engine, η b is the transmission efficiency of the gearbox, η z is the transmission efficiency of the central reducer, η l is the transmission efficiency of the wheel side reducer, area is the tillage area at the instantaneous vehicle speed, that is, the land area that the tractor can plow per hour.

[0091] The land area that the tractor can plow per hour is closely related to the vehicle speed, the tillage width of a single plow body, and the number of plow shares. Therefore, the calculation formula is:

[0092] (6)

[0093] Among them, area is the tillage area at the instantaneous vehicle speed, km²; v cur_spdv is the speed of the tractor, km / h; b0 is the tillage width of a single plow body, converted from cm to m; Z is the number of plowshares, which is 9.

[0094] The actual fuel consumption rate g per unit cultivated area expressed by formula (5) com_enegy_consum , as the vehicle fuel economy index, is used to evaluate the comprehensive energy consumption of the tractor.

[0095] When the tractor speed changes greatly, the fuel economy index is significantly different. Therefore, the road spectrum from 0 to 20 s is selected for comparison of the fuel economy index. Figure 9 It shows the specific fuel consumption g of the tractor from starting from rest to the normal plowing condition from 7.3 s to 20 s in the tractor road spectrum T and the specific fuel consumption per unit area g com_enegy_consum Comparison; Since both evaluation indexes consider the influence of the HMCVT transmission efficiency, and the transmission efficiency is in the denominator position of both formulas. When the tractor starts initially, when the transmission efficiency is infinitesimal and approaches zero, it will cause the problem that the evaluation index cannot be observed. Therefore, the initial start-up stage of the tractor from 0 to 7.3 s is shielded.

[0096] From Figure 9 it can be seen that the specific fuel consumption per unit area index g com_enegy_consum compared with the specific fuel consumption g T has a more obvious numerical width, indicating that considering the cultivated area can more effectively explain the change of the vehicle economy of the tractor.

[0097] Next, the influencing factors of the vehicle fuel economy index are analyzed. In the optimization of the vehicle fuel economy of the energy management strategy of large-horsepower tractors in the past, although the influence of the transmission efficiency of the gearbox on the vehicle fuel economy was considered, and the vehicle fuel economy was optimized through the coupling control of the gearbox and the engine, most of the engine models were established by using the experimental Map diagram method. This method ignores the influence of the change of key internal parameters of the engine on the fuel consumption rate and the output torque, and then ignores the optimization effect of the vehicle fuel economy by jointly optimizing the engine and the HMCVT to improve the power split of the HMCVT. Therefore, in this embodiment, the vehicle fuel economy is jointly optimized through the tractor powertrain model that couples the zero-dimensional model of the dual-fuel engine and the HMCVT model.

[0098] Because of the zero-dimensional model adopted by the dual-fuel engine, the key internal parameters of the engine can also be used as objects for joint optimization research; the fuel injection timing and the fuel injection quantity are important parameters that affect the internal combustion situation and the power performance of the dual-fuel engine. Different combinations of diesel injection timing and the interval between diesel / natural gas injection timing have different effects on the power performance of the engine.

[0099] The injection parameters of three different typical engine working conditions were simulated and analyzed, and the following results were obtained: Figure 10 The schematic diagram of the influence of fuel injection parameters on engine output torque shows that for the same fuel injection interval, the earlier the diesel injection time, the greater the engine output torque tends to be; when the diesel injection time is advanced, the diesel will enter the combustion chamber at an earlier compression stage; on the one hand, this will cause the diesel and air / natural gas mixture to ignite earlier during the compression process, causing the engine to generate higher temperature and pressure at the end of the compression stroke; on the other hand, the early injection of diesel not only helps to effectively ignite natural gas, but may also cause natural gas to start burning at an earlier time; although the flame propagation speed of natural gas is slower, if the diesel is injected too early, it can ensure that the natural gas is fully burned, thereby achieving greater power output.

[0100] With the same diesel injection timing, the engine output torque tends to increase as the fuel injection interval becomes smaller. When the interval between diesel injection and natural gas injection is shortened, the mixture of diesel and natural gas becomes more uniform. A shorter injection interval can cause the two fuels to contact and mix more quickly in the combustion chamber, thereby improving combustion efficiency, generating higher cylinder pressure, and increasing the engine's output torque. If the injection interval is longer, the ignition process of natural gas may lag, resulting in incomplete combustion. A shorter injection interval helps reduce this lag effect, allowing natural gas to ignite more promptly under the ignition of diesel, thereby improving combustion efficiency and enhancing output torque. At the same time Figure 10 It also reflects that the ringing coefficient RI (knock intensity index) of different diesel injection timing and diesel / natural gas injection timing interval combinations is also different, which will have a certain impact on engine knock. A large number of studies believe that if the ringing coefficient RI exceeds 5, the engine has a greater possibility of knock. Therefore, it can be concluded that the engine fuel injection parameters have an impact on the engine output torque / output power, which in turn affects the fuel economy of the vehicle; Figure 11 It can be seen that by changing the fuel injection timing, the fuel economy of the tractor vehicle will be affected to varying degrees. This directly confirms from the perspective of economy that there is a close connection between the engine fuel injection parameters and the fuel economy of the vehicle.

[0101] The engine fuel injection parameters will affect the fuel economy of the whole vehicle. In addition, according to the calculation formula of the vehicle fuel economy index, it can be seen that the transmission efficiency of HMCVT also has a great influence on the fuel economy of the whole vehicle. The transmission efficiency of HMCVT is affected by the displacement ratio of the hydraulic pump-fixed displacement motor and the planetary gear pair. At the same time, power circulation phenomenon will occur in HMCVT, which will reduce the transmission efficiency.

[0102] As can be seen from the above analysis, the fuel economy index of the whole vehicle is affected by various factors as shown in Table 2. By means of the parameter cyclic optimization algorithm for the engine fuel injection parameters and the displacement ratio of the hydraulic pump - fixed displacement motor, the optimal fuel economy of the whole vehicle at different vehicle speeds can be achieved.

[0103] Table 2 Factors Affecting the Fuel Economy of the Whole Vehicle

[0104]

[0105] III. Combined Optimization Algorithm for the Economy of the Powertrain

[0106] The transmission efficiency of the HMCVT is the highest when the displacement ratio is zero, but this is not the optimal point for the fuel economy of the whole vehicle of the tractor powertrain. When the HMCVT speed ratio i changes continuously, there can be countless engine operating conditions coupled with it to meet the load characteristic requirements, but only a unique set of operating conditions is coupled with the HMCVT, and the obtained fuel economy index of the whole vehicle is the best.

[0107] In order to keep the fuel economy of the whole vehicle at the best during the operation of the large - horsepower tractor under any working conditions, combining the dual - fuel engine model and the analysis method of the HMCVT transmission efficiency, through the NSGA - II multi - objective genetic algorithm and the parameter cyclic optimization algorithm, a combined optimization algorithm for the economy of the powertrain is formed to carry out the combined optimization of the economy of the tractor powertrain under any working conditions. Through this algorithm, the optimal engine fuel injection parameters and the HMCVT displacement ratio of the tractor under any working conditions in the load characteristic field can be calculated, ensuring the engineering realization of optimizing the fuel economy index of the tractor whole vehicle.

[0108] When solving the optimal control parameters, the basic parameters of the tractor / implement, the HMCVT parameters, and the road spectrum information are used as the basic parameters of the algorithm, while the expected vehicle speed, the number of plowshares, and the HMCVT speed ratio, which characterize the current working state of the tractor, are used as input variables to select a set of optimal powertrain control parameters from the set of optimal powertrain control parameters obtained by solving; among them, the set of optimal powertrain control parameters is composed of the optimal powertrain control parameters corresponding to the combinations of different expected vehicle speeds, the number of plowshares, and the HMCVT speed ratio.

[0109] When solving the set of optimal powertrain control parameters, a combined optimization is carried out for each combination of the expected vehicle speed, the number of plowshares, and the HMCVT speed ratio, that is, parameter cyclic optimization.

[0110] Among them, the main components of the powertrain are composed of the engine, the HMCVT, the central drive, and the wheel - side drive system. The central drive and the wheel - side drive system are both composed of meshing gears, and their transmission efficiency can be regarded as a fixed value. Therefore, the transmission efficiency of the powertrain is determined by the efficiency of the engine and the HMCVT. In each cycle of this embodiment, through the designed fuel economy index g of the whole vehiclecom_enegy_consum To characterize the pros and cons of the powertrain efficiency, the vehicle fuel economy index is used as one of the objective functions of the joint optimization algorithm. At the same time, the load always exists during the vehicle's driving process. As the ratio of the HMCVT changes, the required torque of the HMCVT also changes continuously. The engine output torque needs to meet the required torque of the HMCVT for the tractor to drive under the current working condition. If the engine output torque does not meet the current required torque of the HMCVT, the throttle opening needs to be increased to increase the engine output torque. Therefore, minimizing the difference between the required torque of the HMCVT and the actual engine output torque is taken as the second objective function of the optimization algorithm.

[0111] Design the code of the joint optimization algorithm for powertrain economy on the software platform. In this embodiment, Matlab2022b can be selected for code design. Taking the plowing working condition as an example, as Figure 12 shown, the specific steps of the optimization algorithm are as follows:

[0112] Step S2.1 Basic parameter setting:

[0113] In the joint optimization process, basic settings need to be made for multiple parameters of the powertrain model, including parameter settings for the tractor, agricultural implements, and HMCVT, and obtaining the vehicle speed change range under the plowing working condition through road spectrum information.

[0114] Among them, the tractor parameters include the driving wheel radius r, the operating mass m0 of the tractor, the cultivated area per unit time S at the instantaneous vehicle speed area , the main transmission ratio i central , the final drive ratio i wheel , etc.; the agricultural implement parameters include the maximum number of plowshares n plowsharemax that can be mounted, the minimum number of plowshares n plowsharemin that can be mounted, the specific resistance of the soil k0, the tillage depth h0, the tillage width b0 of a single plow body; the HMCVT parameters include the maximum and minimum values of the ratio i max , i min , the characteristic parameters k1, k2 of the planetary gear; the road spectrum information includes the maximum and minimum values of the plowing vehicle speed v tractormax , v tractormin .

[0115] Step S2.2 Discretization of the tractor working state under the plowing working condition:

[0116] Discretize the plowing vehicle speed read from the road spectrum information. At the same vehicle speed, the number of plowshares that the tractor can carry is different, and different combinations of the HMCVT ratio and the engine working condition can achieve the same plowing vehicle speed; therefore, discretize the number of plowshares and the HMCVT ratio. By discretizing the plowing vehicle speed v tractor , the number of plowshares n plowshare , and the HMCVT ratio ihmcvt By discretizing, the coupling situation of the powertrain control parameters at each plowing working condition vehicle speed of the tractor can be obtained, that is, the expected vehicle speed v tracto , the number of plowshares n plowshare , the ratio i of the HMCVT hmcvt For different combinations, perform the following parameter loop optimization on different combinations.

[0117] Step S2.3 Judge the engine speed:

[0118] From the discretized parameter combinations obtained in step S2.2, applying the tractor, implement, HMCVT parameters, and road spectrum information set in step S2.1, the working engine speed n eng can be calculated for each combination. Judge the working engine speed n eng Whether it is within the normal working speed range [n engmin , n engmax of the engine. If it is, continue downward; otherwise, if it exceeds the range, return to step S2.2 to obtain the next set of discretized parameter combinations.

[0119] Step S2.4 Calculate the required torque T cvtout of the HMCVT and the transmission efficiency η b :

[0120] According to the discretized parameter combinations obtained in step S2.2, the internal gear meshing fixed relationship of the gearbox, and its continuously variable transmission law, using the ratio i hmcvt of the HMCVT and the characteristic parameters k1, k2 of the planetary gear, calculate the displacement ratio e.

[0121] According to the operating mass m0 of the tractor, the number of mounted plowshares n plowshare , the specific draft k0 of the soil, the tillage depth h0, the tillage width b0 of a single plow body, and the cultivated land area S per unit time of the instantaneous vehicle speed area , calculate the current resistance F z of the tractor.

[0122] Apply the calculated displacement ratio e, the original discretized ratio i hmcvt of the HMCVT, and the characteristic parameters k1, k2 of the planetary gear to calculate the torque magnitude T engneed that the engine needs to provide.

[0123] Then, according to the calculated current resistance F z of the tractor, the main transmission ratio i central , and the final drive ratio i wheel , calculate the torque T cvtout at the output end of the gearbox.

[0124] Finally, through the torque input and output from the transmission (the torque that needs to be provided by the engine) and the torque T at the output end of the transmission cvtout , the transmission efficiency η at this time is obtained b , and the relevant results regarding the HMCVT are passed to step S2.5.

[0125] In step S2.5, through the discretized combination of parameters obtained in steps S2.1 / S2.2 / S2.3, the corresponding fuel injection parameters are read from the basic data table of engine fuel injection parameters, and the read data is passed to step S2.6 as the initial value of the fuel injection parameters.

[0126] Step S2.6 performs parallel calculation using the NSGA-II multi-objective genetic algorithm:

[0127] According to the initial values of the fuel injection parameters under each discretized combination of parameters obtained in step S2.5, the dynamic change of the boundary of the individual gene values within the genetic algorithm is realized.

[0128] During the algorithm optimization process, the individuals within the population discretize the fuel injection parameters. The discretized combination of fuel injection parameters obtained using the dynamic boundary is more in line with the actual engine fuel injection process. At the same time, the discretized combination of fuel injection parameters with the dynamic boundary can effectively improve the vehicle fuel economy index affected by the HMCVT power cycle; during the optimization process, to shorten the optimization time, parsim is used to perform parallel calculation on the individuals with different parameter combinations in the cell array, and finally the calculation speed of the model is significantly improved.

[0129] Among them, the objective function of the NSGA-II multi-objective genetic algorithm: to make full use of the computing resources and save the optimization time for the objective function, the discretized combination individuals of the fuel injection parameters obtained in step S2.6 are subjected to model parallel simulation through parsim, and the simulation calculation is carried out jointly with the Simulink powertrain model. Through this method, the optimization time can be effectively shortened, and the optimization of the objective functions shown in formulas (7) and (8) can be achieved as much as possible within a relatively short time. By using formulas (7) and (8) to calculate the two objective function values, the vehicle fuel economy index g of each discretized combination individual of the fuel injection parameters is calculated com_enegy_consum (diesel, gas, diesel inj , gas inj ) and the engine output torque T (diesel, gas, diesel inj , gas inj ), and the data is passed back to the.m optimization algorithm file in Matlab.

[0130] (7)

[0131] (8)

[0132] Among them, diesel is the diesel injection quantity; gas is the natural gas injection quantity; diesel inj is the diesel injection timing; gas inj is the natural gas injection timing; T HMCVT_in is the engine output, the torque input to the transmission.

[0133] Based on the above formula, with the goal of minimizing the actual fuel consumption rate per unit cultivated land area and minimizing the difference between the HMCVT required torque and the actual engine output torque, the optimal powertrain control parameters for each combination are solved. Each combination may obtain multiple sets of optimal powertrain control parameters. In this embodiment, the ringing coefficient RI is used as the screening condition to preliminarily screen the optimization results.

[0134] Through the simplification and assumption of the data obtained from the pressure oscillation frequency experiment and the scaling of the pressure data, the researchers found that the pressure wave amplitude is proportional to the maximum pressure rise rate, as shown in formula (9). According to the fact that the pressure wave energy density is proportional to the square of the pressure pulsation amplitude and the sound speed in the medium and inversely proportional to the average system pressure, a cylinder pressure oscillation evaluation index - ringing coefficient RI as shown in formula (10) is deduced. Except for γ, all parameters can be measured through the zero-dimensional engine model simulation process, which proves that the engine model has the ability to consider multiple factors such as knocking, and has more usage scenarios and application capabilities compared to the Map building model.

[0135] (9)

[0136] (10)

[0137] In the formula, ΔP is the rate of pressure rise at explosion; P max is the explosion pressure; T max is the in-cylinder explosion temperature; R is the specific heat ratio, numerically equal to the isentropic exponent K; γ is the proportionality coefficient, calculated from the experimental data, and here it is taken as 0.001.

[0138] According to the analysis of the influencing factors of the vehicle fuel economy index, it can be known that different discrete combinations of fuel injection parameters will have a certain impact on engine knocking. Therefore, during the model simulation process, the knocking intensity in the engine cylinder is characterized by calculating the ringing coefficient RI. It is determined that when RI is greater than 5, the engine undergoes knocking. The ringing coefficient is used as the screening condition for the best individuals on the Pareto front of the NSGA-II multi-objective genetic algorithm optimization to reasonably screen the optimization results.

[0139] After preliminary screening using the ringing coefficient, the total objective function value of each optimized individual is calculated by taking the weighted average (both weights are 0.5) of two objective functions. The individual with the smallest value is selected as the optimal individual, that is, a set of optimal powertrain control parameters is retained for each combination.

[0140] The optimal powertrain control parameters of all combinations form an optimal powertrain control parameter set. According to the expected vehicle speed, number of ploughshares, and HMCVT speed ratio of the current working state, the corresponding optimal powertrain control parameters are selected from the set as the final powertrain control parameters of the target tractor.

[0141] In this embodiment, the economic joint optimization results of key parameters are verified.

[0142] Using the above joint optimization algorithm, the optimal powertrain control parameters of large - horsepower tractors under the regulation of economic control strategies can be obtained, and the joint optimization result diagram as shown in Figures 13 - 18 is obtained. It reflects the engine fuel injection parameters with the best vehicle fuel economy under different vehicle speeds and HMCVT speed ratios, which can be stored separately in actual control and will no longer change; for different vehicle speeds and HMCVT speed ratios, only the best - matched fuel injection parameters are needed, which can reduce the data storage volume in the controller and make the engineering implementation simpler; the above optimization results are plotted as a two - dimensional Map of vehicle speed - HMCVT speed ratio and coupled into the engine model inside the tractor vehicle control model. Combining with the internally developed online neural network prediction model, the optimal fuel injection parameters for any speed ratio - vehicle speed combination can be achieved; as Figures 13 - 18 can be seen, as the ploughing vehicle speed of the tractor increases, the variable amplitude range of the HMCVT speed ratio becomes larger, indicating that as the vehicle speed increases, there is a greater optimization space for the economy of the powertrain.

[0143] In this embodiment, the fuel economy before and after optimization is also compared. Since the vehicle fuel economy index has a more obvious change trend during the acceleration stage of the tractor ploughing condition, which can effectively reflect the vehicle fuel consumption of the tractor, the road spectrum from 0 to 20 s is selected for comparative verification. Figures 19 - 20 It shows the specific fuel consumption per unit area g of the fuel injection parameters before and after the application of the joint optimization of the tractor during the period from 7.3 s to 20 s of the tractor road spectrum, starting from a stationary state to the normal ploughing condition. com_enegy_consum The problem that the evaluation index cannot be observed will occur when the HMCVT transmission efficiency is infinitesimal and approaches zero. The fuel economy index during the initial start - up stage of the tractor from 0 to 7.3 s is shielded. Among them, Figure 19 It shows the comparison of the vehicle economy before and after the model optimization with the specific fuel consumption (a traditional common vehicle economy index) shown in formula (5) as the observable quantity. It can be seen that the actual specific fuel consumption is reduced by up to 18.06% after the joint optimization, and the vehicle has better economy.Figure 20 It shows the comparison of the vehicle fuel economy before and after the model optimization with the economic evaluation index established in this embodiment as the observed quantity. It can be seen that the instantaneous specific fuel consumption per unit area is reduced by up to 12.26% after the combined optimization. By adopting the combined optimization algorithm of the engine and HMCVT, the power cycle problem that cannot be avoided when optimizing the tractor HMCVT alone is successfully solved, and the vehicle fuel economy performance is effectively improved from the perspective of the combined optimization of the powertrain.

[0144] Embodiment 2

[0145] In an embodiment of the present disclosure, a combined optimization system for a tractor powertrain based on economic indicators is provided, including:

[0146] A model construction module, configured to: build a tractor powertrain model by forward simulation based on the structural information and operating conditions of the target tractor;

[0147] A vehicle speed control module, configured to: use the tractor powertrain model to feedback control the actual vehicle speed of the target tractor following the desired vehicle speed;

[0148] Wherein, during the control process, the actual fuel consumption rate per unit cultivated land area is used as the vehicle fuel economy index, and the economic combined optimization of the powertrain control parameters is carried out to obtain the optimal powertrain control parameters of the target tractor under the optimal economic index. The powertrain control parameters include the fuel injection parameters of the engine and the displacement ratio of the hydro-mechanical continuously variable transmission.

[0149] Embodiment 3

[0150] In an embodiment of the present disclosure, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, it implements the combined optimization method for a tractor powertrain based on economic indicators.

[0151] Embodiment 4

[0152] In an embodiment of the present disclosure, a non-transitory computer-readable storage medium is provided. The non-transitory computer-readable storage medium is used to store computer instructions, and when the computer instructions are executed by a processor, the combined optimization method for a tractor powertrain based on economic indicators is implemented.

[0153] Embodiment 5

[0154] In one embodiment of the present disclosure, an electronic device is provided, including: a processor, a memory, and a computer program; wherein, the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device runs, the processor executes the computer program stored in the memory so that the electronic device executes the method for jointly optimizing the tractor powertrain based on economic indicators as described above.

[0155] The present disclosure is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present disclosure. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processors of general-purpose computers, special-purpose computers, embedded processors, or other programmable data processing devices to generate a machine, so that the instructions executed by the processors of the computer or other programmable data processing devices generate means for realizing the functions specified in one Figure 1 flow or multiple flows and / or blocks Figure 1 or means for realizing the functions specified in one block or multiple blocks.

[0156] Although the specific embodiments of the present disclosure have been described above in conjunction with the accompanying drawings, it is not a limitation on the protection scope of the present disclosure. Those skilled in the art should understand that, based on the technical solutions of the present disclosure, various modifications or deformations that can be made by those skilled in the art without creative efforts are still within the protection scope of the present disclosure.

Claims

1. A combined optimization method for the tractor powertrain based on economic indicators, characterized in that Including: Based on the structural information and working conditions information of the target tractor, a tractor powertrain model is built using forward simulation; Through the tractor powertrain model that couples the zero-dimensional model of the dual-fuel engine and the HMCVT model, the fuel economy of the whole vehicle is jointly optimized; Using the tractor powertrain model, following the desired vehicle speed, the actual vehicle speed of the target tractor is feedback-controlled; Wherein, during the control process, the actual fuel consumption rate per unit cultivated land area is used as the fuel economy index of the whole vehicle, and the powertrain control parameters are jointly optimized economically to obtain the best powertrain control parameters of the target tractor under the optimal economic index. The powertrain control parameters include the fuel injection parameters of the engine and the displacement ratio of the hydro-mechanical continuously variable transmission; the fuel injection parameters include the fuel injection timing, the air injection timing, the fuel injection quantity and the air injection quantity; The vehicle fuel economy index incorporates the actual working condition factor of cultivated land area into the economy index, and is expressed by the formula: ; Among them, is the actual fuel consumption rate per unit cultivated land area, g e is the fuel consumption rate of the engine, η b is the transmission efficiency of the gearbox, η z is the transmission efficiency of the central reducer, η l is the transmission efficiency of the wheel side reducer, and area is the cultivated land area at the instantaneous vehicle speed.

2. The combined optimization method of the tractor powertrain based on economic indicators according to claim 1, characterized in that, The tractor powertrain model includes a plowing operation condition model, an HMCVT controller, a driver model, a power system model and a vehicle dynamics model of the tractor; Wherein, the plowing operation condition model provides the desired speed of the plowing operation; The HMCVT controller controls the displacement ratio and the transmission gear according to the difference between the desired vehicle speed in the plowing operation condition and the actual vehicle speed; The driver model calculates and outputs the opening of the accelerator pedal according to the difference between the desired vehicle speed and the actual vehicle speed and the difference between the HMCVT required torque and the actual engine output torque; The power system model controls the engine output torque and the HMCVT output speed according to the outputs of the HMCVT controller and the driver model, obtains the torque of the HMCVT, and inputs it to the vehicle dynamics model of the tractor; The vehicle dynamics model of the tractor calculates the wheel speed at each moment based on the torque of the HMCVT to obtain the actual speed of the tractor.

3. The combined optimization method of the tractor powertrain based on economic indicators according to claim 1, wherein, The economic joint optimization is to constitute an economic joint optimization algorithm for the tractor powertrain through parameter cyclic optimization and the NSGA-II multi-objective genetic algorithm, and economically optimize the tractor powertrain under the plowing operation condition.

4. The combined optimization method of the tractor powertrain based on economic indicators according to claim 1, characterized in that The specific steps of the economic joint optimization are as follows: Perform basic settings on the multi-parameters of the tractor powertrain model; Discretize the working state of the tractor under the plowing operation condition to obtain different combinations of the desired vehicle speed, the number of plowshares, and the HMCVT speed ratio, and perform parameter cyclic optimization on different combinations: through the NSGA-II multi-objective genetic algorithm, with the minimum actual fuel consumption rate per unit cultivated land area and the minimum difference between the HMCVT required torque and the actual engine output torque as the objectives, solve the optimal powertrain control parameters under each combination to obtain the optimal powertrain control parameter set; Obtain the current working state of the target tractor, and use the combination of the desired vehicle speed, the number of plowshares, and the HMCVT speed ratio corresponding to the current working state to select the corresponding optimal powertrain control parameters from the optimal powertrain control parameter set.

5. Tractor powertrain joint optimization system based on economic indicators, characterized in that Including: A model construction module, configured to: based on the structural information and working conditions information of the target tractor, build a tractor powertrain model using forward simulation; Through the tractor powertrain model that combines the zero-dimensional model of the coupled dual-fuel engine and the HMCVT model, the fuel economy of the whole vehicle is jointly optimized; A vehicle speed control module, configured to: use the tractor powertrain model to perform feedback control on the actual vehicle speed of the target tractor following the desired vehicle speed; Among them, in the control process, the actual fuel consumption rate per unit cultivated land area is used as the fuel economy index of the whole vehicle, and the control parameters of the powertrain are jointly optimized economically to obtain the optimal powertrain control parameters of the target tractor under the optimal economy index. The powertrain control parameters include the fuel injection parameters of the engine and the displacement ratio of the hydro-mechanical continuously variable transmission; the fuel injection parameters include the fuel injection timing, the air injection timing, the fuel injection quantity, and the air injection quantity; The vehicle fuel economy index incorporates the actual working condition factor of cultivated land area into the economy index, and is expressed by the formula as follows: ; Among them, is the actual fuel consumption rate per unit cultivated land area, g e is the fuel consumption rate of the engine, η b is the transmission efficiency of the gearbox, η z is the transmission efficiency of the central reducer, η l is the transmission efficiency of the wheel side reducer, and area is the cultivated land area at the instantaneous vehicle speed.

6. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the tractor powertrain joint optimization method based on the economy index according to any one of claims 1-4.

7. A non-transitory computer-readable storage medium, characterized in that, The non-transitory computer-readable storage medium is used to store computer instructions, and when the computer instructions are executed by the processor, it implements the tractor powertrain joint optimization method based on the economy index according to any one of claims 1-4.

8. An electronic device, characterized in that, Including: A processor, a memory, and a computer program; wherein, the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device runs, the processor executes the computer program stored in the memory so that the electronic device executes the tractor powertrain joint optimization method based on the economy index according to any one of claims 1-4.

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